Data visualization with ggplot2

# load the tidyverse library (which includes dplyr and ggplot2)
library(tidyverse) # or library(ggplot2) and library(dplyr)
# load the gapminder dataset for this lesson
gapminder <- read.csv("data/gapminder_data.csv")

Ggplot2 is built on the grammar of graphics which builds plots in layers.

Let’s start off with an example:

# use ggplot to initialize a plot of gapminder's gdpPercap (x) and lifeExp (y)
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  # add a scatterplot (points) layer
  geom_point()

The two top-level functions we have used are ggplot() and geom_point().

Notice the use of + to add a layer.

ggplot():

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))

This function lets R know that we’re creating a new plot, and any of the arguments we give the ggplot function apply to all layers of our plot.

We’ve passed in two arguments to ggplot:

  1. data = gapminder: tells ggplot what data we want to show on our figure

  2. mapping = aes(x = gdpPercap, y = lifeExp): tells ggplot how variables in the data should map to aesthetic properties (e.g., the x and y coordinates).

geom_point()

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

geom_point() adds a scatterplot using the data and global aesthetics we specified in ggplot().

Challenge 1

Create a scatterplot of GDP per capita (x) versus lifeExp (y) using just the gapminder data from 2007.

Hint: use the pipe to pipe the output of a filter() function into the ggplot() function

To use just the gapminder data from 2007, we can use filter() to filter to just 2007 and then pipe the results of this filtering into the first argument of ggplot()

gapminder |>
  filter(year == 2007) |>
  ggplot(mapping = aes(x = gdpPercap, y = lifeExp)) + 
  geom_point()

Challenge 2

In the previous examples and challenge we’ve used the aes function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color.

Modify the code from the previous challenge to color the points by the “continent” column. What trends do you see in the data? Are they what you expected?

The solution presented below adds color=continent to the call of the aes function. The general trend seems to indicate an increased life expectancy over the years. On continents with stronger economies we find a longer life expectancy.

gapminder |>
  filter(year == 2007) |>
  ggplot(mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) + 
  geom_point()

Captions via code chunk options

You can add a caption to a figure in a quarto document by supplying a label and fig-cap quarto chunk option:

gapminder |>
  filter(year == 2007) |>
  ggplot(mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) + 
  geom_point()

Figure 1: GDP per capita vs life expectancy

Let’s compile our document to check that a caption appeared for this figure!

Line plots

Let’s try to visualize life expectancy for each country over time, coloring our lines by continent

# use ggplot() to plot lifeExp versus year as a line plot 
# and try to color the lines by continent
ggplot(gapminder, aes(x = year, y = lifeExp, color = continent)) +
  geom_line()

Our plot looks strange… what’s going on in this plot?

We haven’t told ggplot that we want a separate line for each country.

We can do that by adding a group argument inside the aes() function:

# use ggplot() to plot lifeExp versus year as a line plot and group by country
# and try to color the lines by continent
ggplot(gapminder, aes(x = year, y = lifeExp, color = continent, group = country)) +
  geom_line()

Multiple geom layers

We can visualize both lines and points on the same plot by adding multiple geom_() layers:

# Add a points layer to the line plot above
ggplot(gapminder, aes(x = year, y = lifeExp, group = country, color = continent)) +
  geom_line() + 
  geom_point()

Supplying local layer aesthetics

In the example above, the aesthetics (from aes()) are applied to both layers.

To apply an aesthetic just to one layer, you can supply a separate aes() function to the layer:

# recreate the plot above but apply the color aesthetic just to the lines layer
ggplot(gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(aes(color = continent)) + 
  geom_point()

The order of the layers

Each layer is drawn on top of the previous layer. What happens if we switch the order of the layers?

# Rewrite the code above but with the points layer and line layer in the opposite order
ggplot(gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_point() +
  geom_line(aes(color = continent))

Setting aesthetics to a unfiform (non-data) value

To change the aesthetic of all lines/points to a value that is not dictated by the data, you may think that aes(color="blue") should work, but it doesn’t.

Let’s try to set the color of our lines to “blue”:

# create the same line plot as above of year vs lifeExp for each country,
# but try to set the color of all of the lines to "blue" inside aes():
ggplot(gapminder) +
  geom_line(aes(x = year, y = lifeExp, group = country, color = "blue"))

When setting an aesthetic to a value that does not correspond to a variable from our data, we need to move the color specification outside of the aes() function:

# fix the above code by moving the `color` argument outside `aes()`
ggplot(gapminder) +
  geom_line(aes(x = year, y = lifeExp, group = country), color = "blue")

Transparency

Another aesthetic value that is helpful is adding transparency using alpha:

# Add transparency (alpha = 0.2) to the previous line plot of year vs life exp
ggplot(gapminder) +
  geom_line(aes(x = year, y = lifeExp, group = country), 
            color = "blue", alpha = 0.2)

Transformations

Recall our scatterplot of gdpPercap vs lifeExp (this time with transparency):

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5)

Let’s add a scale layer to present the x-axis on a log10 scale:

# add a log-10 scale for the x-axis from the previous plot
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) +
  scale_x_log10()

Adding a linear fit

We can also fit a simple relationship to the data by adding another layer, geom_smooth():

# add a lm smooth layer to the previous plot
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) +
  scale_x_log10() +
  geom_smooth(method = "lm")
`geom_smooth()` using formula = 'y ~ x'

Try changing the linewidth using the linewidth argument.

Challenge 4a

Modify the following code so that all of the points are colored “orange” and have size equal to 3.

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm") +
  scale_x_log10()
`geom_smooth()` using formula = 'y ~ x'

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5, color = "orange", size = 3) +
  geom_smooth(method = "lm") +
  scale_x_log10()
`geom_smooth()` using formula = 'y ~ x'

Challenge 4b

Modify your solution to Challenge 4a so that the color of the points layer (but not the smooth layer) is instead determined by the continent variable and the size is determined by the pop variable. You should only have one smooth line in your final plot.

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop),
             alpha = 0.5) +
  geom_smooth(method = "lm") +
  scale_x_log10()
`geom_smooth()` using formula = 'y ~ x'

Multi-panel figures

Earlier we visualized the change in life expectancy over time across all countries in one plot like this:

ggplot(gapminder, aes(x = year, y = lifeExp, color = continent, group = country)) +
  geom_line()

Another way to view this data is to create a separate plot for each continent.

One way to do this would be to create a separate plot for each continent manually. For example:

# create a line plot for the countries in the Americas only
gapminder |> 
  filter(continent == "Americas") |>
  ggplot(aes(x = year, y = lifeExp, group = country)) +
  geom_line()

# create a line plot for all of countries in Europe only
gapminder |> 
  filter(continent == "Europe") |>
  ggplot(aes(x = year, y = lifeExp, group = country)) +
  geom_line()

But there is a more efficient way to do this using facet_wrap():

# create a grid of line plots of year vs lifeExp for the countries in each continent
# using facet_wrap()
ggplot(gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line() +
  facet_wrap(~continent)

Challenge 5

Create a faceted set of line plots for life expectancy versus year for each country in the Americas (e.g., each facet will contain the individual line plot for a single country in the Americas).

gapminder |>
  filter(continent == "Americas") |>
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap(~country)

Modifying labels

You can add labels to plots using the labs() function.

gapminder |> 
  filter(country == "Brazil") |>
  ggplot() +
  geom_line(aes(x = year, y = lifeExp)) 

# Add reasonable labels to the plot above
  labs(x = "Year",              
       y = "Life expectancy",   
       title = "Life expectency by year in Brazil")
$x
[1] "Year"

$y
[1] "Life expectancy"

$title
[1] "Life expectency by year in Brazil"

attr(,"class")
[1] "labels"
Challenge 6

Using geom_boxplot(), generate boxplots to compare life expectancy between the different continents (set x = continent and y = lifeExp), faceted by year.

Color each boxplot by continent and rename each label so that it is nicely formatted and human-readable.

Here a possible solution:

gapminder |> 
  ggplot(aes(x = continent, y = lifeExp, fill = continent)) +
  geom_boxplot() + 
  facet_wrap(~year) +
  labs(x = "Continent",
       y = "Life Expectancy",
       fill = "Continent")

Built-in themes

There are several themes for making your plots even prettier. For example,

  • theme_classic():
# add theme_classic() to the following plot
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop, color = continent)) +
  scale_x_log10() +
  theme_classic()

  • theme_minimal():
# add theme_minimal() to the following plot
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop, color = continent)) +
  scale_x_log10()  +
  theme_minimal()

  • theme_bw():
# add theme_bw() to the following plot
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop, color = continent)) +
  scale_x_log10()  +
  theme_bw()